High-precision sand thickness data are fundamentally important for optimizing exploration strategies in petroleum geology. In the Chengbei work area of the Jiyang Depression, the stratigraphic channels are chaotically developed, with channels of varying sizes in different strata overlapping, intersecting, and exhibiting narrow widths. The actual well-seismic relationship is poor. Therefore, individual seismic attributes in this area exhibit extremely low correlation with channel sandstone thickness. Conventional attributes such as root mean square amplitude show no distinct channel characteristics, necessitating the integration of multiple seismic attributes for effective prediction. Moreover, the high multicollinearity among seismic attributes introduces significant interference in prediction results. Therefore, this study integrates the Pearson correlation coefficient and variance inflation factor (VIF) to optimize seismic attribute selection, effectively eliminating redundant attributes and those with low correlation. To further enhance prediction accuracy and address the significant bias inherent in single-model predictions, this study introduces the ensemble learning XGBoost model, which integrates predictions from multiple weak learners to improve the precision of sandstone thickness estimate. The Newton–Raphson-based optimization algorithm was employed to fine-tune the XGBoost parameters. Results from test wells demonstrate a remarkable improvement in prediction accuracy, achieving reliable sandstone thickness estimation despite poor well-seismic correlations. This research provides valuable insights and offers a widely applicable methodology for predicting the thickness of complex channel sand bodies.